جست‌وجوی استاد و پژوهشگر

• Visualization
• Computer-Aided Geometric Design
• Computer-Aided Design
• +۲ مورد دیگر

Georges-Pierre Bonneau is a Professor at University of Grenoble conducting research at Laboratoire Jean Kuntzmann (LJK) and INRIA in the MAVERICK research group. His academic career spans over three decades, beginning with his PhD from Universität Kaiserslautern in 1993, followed by positions as CNRS researcher (1994-2001), and Professor at University of Grenoble since 2001. Dr. Bonneau's research focuses on scientific visualization, computer-aided geometric design (CAGD), computer-aided design (CAD), and computational fabrication techniques. His work bridges theoretical geometric modeling with practical applications in structural monitoring, energy consumption visualization, and digital fabrication. He has developed innovative methods for surface reconstruction from sparse data, bending-active structures, and visualization of uncertain scalar data fields. His recent publications demonstrate a strong focus on immersive 3D modeling techniques, computational fabrication methods, and advanced visualization systems. Bonneau's research shows consistent evolution from foundational geometric modeling work in the 1990s-2000s toward contemporary applications in VR/AR interfaces, computational design of physical structures, and energy consumption visualization systems. Throughout his career, Bonneau has actively contributed to the academic community as conference organizer and program committee member for major venues including SPM, SMI, Eurographics, and IEEE Visualization conferences. He has supervised numerous PhD students whose work spans diverse applications of geometric modeling and visualization, with recent students focusing on surface creation from sparse strokes, bending-active structures, and structural monitoring systems. His former students have gone on to successful careers at institutions including AAC Clyde Space, Zurich Insurance, Pixminds, ISIR, and CEA.

• Cybersecurity
• Android Application Security
• Embedded Software Security
• +۶ مورد دیگر

Jean-Francois Lalande is a Professor at CentraleSupélec , affiliated with the Inria PIRAT team . His research focuses on IT infrastructure security, Android application security, and embedded C software security, with specific interests in access control policies, intrusion detection tools, and software code analysis. Participated in PEPR DefMal (2022–2028): Defense Against Malware Coordinated CominLabs Kharon (2014–2018): Android Malware Monitoring His recent publications emphasize Android malware analysis, smart card security, and privacy-preserving protocols. Key trends include code obfuscation, information flow tracking, and empirical evaluation of security tools. He has served as co-chair for multiple workshops including the International Workshop on Information Security Methodology and Replication Studies (2019–2022) and organized the Cybersecurity Exercise for Red and Blue team Entertainment (CERBERE). Lalande advises PhD students in cybersecurity domains and collaborates on projects addressing Android malware detection, static analysis tools, and security verification.

• Digital Content Management
• Information Retrieval
• Semantic Analysis
• +۳ مورد دیگر

Nesrine Ben Mustapaha is a Researcher affiliated with the RIADI-GDL Laboratory at the National School of Computer Sciences in Tunisia. Her research focuses on managing heterogeneous digital content, addressing challenges in structuring knowledge across textual, graphic, and voice data to bridge semantic gaps in information retrieval. She emphasizes adaptive and intelligent systems for personalized information filtering. Her work has been presented at events such as the 15th IAPR International Workshop on Document Analysis Systems (DAS 2022) and the CICLing 2019 conference in La Rochelle. She has participated in numerous academic gatherings, including thesis defenses, workshops on AI applications, and scientific days organized by institutions like the MIRES Federation and the L3i laboratory. Key contributions include advancing socio-semantic search methodologies and exploring interdisciplinary approaches to data integration. Her involvement in initiatives like CircularSeas and NewsEye highlights her commitment to innovation in digital humanities and data-driven solutions.

• Social Signal Processing
• Human-Computer Interaction
• Group Dynamics
• +۳ مورد دیگر

Beatrice Biancardi is an Associate Professor at CESI, affiliated with the Learning and Innovating research team. Her work focuses on Social Signal Processing, Human-Computer Interaction, and Virtual Agents, with a strong emphasis on non-verbal behavior analysis and group dynamics. She holds a PhD in Human-Computer Interaction from Sorbonne University (2016-2019) and has co-supervised multiple PhD theses in areas like AI-driven collective intelligence and public speaking feedback systems. Education: PhD in Human-Computer Interaction, Sorbonne University (2016–2019) International Master in Cognitive Science, University of Trento (2013–2015) Bachelor in Applied Cognitive Psychology, University of Trento (2010–2013) Research Highlights: Biancardi’s research explores how virtual agents and AI can enhance human interaction in immersive environments. Key projects include developing the 3MT_French dataset for public speaking analysis and investigating transactive memory systems in collaborative teams. Her work bridges cognitive science, social signal processing, and technological innovation. Recent Publications Trends: Her articles emphasize multimodal interaction analysis, virtual agent design, and AI’s role in education and human judgment. Notable contributions include studies on public speaking persuasion, human-likeness in AI agents, and team collaboration frameworks. Advising & Grants: She co-supervises three active PhD theses, including Tristan Lannuzel’s work on AI-driven collective intelligence and Alisa Barkar’s research on public speaking feedback systems. Her collaborative projects involve institutions like Inria Paris and Télécom Paris. Labs/Teams: Her primary affiliation is with CESI LINEACT’s Learning and Innovating team, focusing on applied research in virtual environments and human-agent interaction.

Marie-Paule Caniمشاهده پروفایل

استاد

• Computer Graphics
• Geometric Modeling
• Computer Animation
• +۸ مورد دیگر

Marie-Paule Cani is a Professor of Computer Science at École Polytechnique since May 2017, previously affiliated with Grenoble Institute of Technology (INP) and INRIA until 2017. She leads the STREAM research team (Structure Representation and Extraction for computer Animation and geometric Modeling) and was head of the IMAGINE project team (joint LJK/INRIA). Her work bridges geometric modeling, computer animation, and intuitive 3D content creation methods.

• Educational Technology
• Learning Design
• Serious Games
• +۳ مورد دیگر

Valérie Emin-Martinez is a Researcher at the École Normale Supérieure de Lyon (France) within the Institut français de l'Éducation. She holds a PhD in Computer Science from Joseph Fourier University (Grenoble 1), focusing on intention-driven modeling for educational scenarios. Her work integrates digital technologies into pedagogical design, emphasizing game-based learning and scenario authoring tools like ScenEdit. Since 2008, she has coordinated research on pedagogical scenario design in science and technology education, collaborating with the GALA network of excellence for Serious Games. Education: PhD in Computer Science (2010, Joseph Fourier University), DEA in Artificial Intelligence (1990, Paris VI). Professional Experience includes roles as a Certified Professor of Economics & IT (1994–2015) and Research Officer at INRP/IFE. Key projects include the ANR JEN.lab initiative and EU-funded STELLAR network research on collaborative learning orchestration. Research interests span scenario design frameworks, serious games, and analysis of learning traces in hybrid environments. She has published extensively in journals like International Journal of Learning Technologies and conferences such as ECGBL and ICALT, focusing on pedagogical patterns, learning design tools, and teacher empowerment through technology. Her contributions include the ISiS model and ScenEdit tool, advancing reusable components for teachers. Current work explores cross-space learning activities and teacher-led design inquiry in game-based pedagogy.

• Statistics
• Machine Learning
• Data Science
• +۳ مورد دیگر

Diego Nascimento is an Assistant Professor in Statistics at NEOMA Business School. His research focuses on statistical modeling, machine learning, spatio-temporal dependencies, and data visualization, with applications in supply chain management, neurosciences, and industrial projects. He has published in top-tier journals like the Journal of the Royal Statistical Society Series C and Pattern Recognition , collaborating with companies in South America and healthcare institutions. His work bridges theoretical statistics with practical industry challenges, emphasizing Bayesian methods and dynamic models. Key research areas include spatio-temporal modeling, neural data analysis, and optimization techniques. Recent studies involve clinical trials on transcranial stimulation effects and predictive analytics for supply chains during the pandemic. He actively contributes to interdisciplinary projects, combining statistical rigor with real-world applications in healthcare and environmental monitoring. Award-winning research includes advancements in imbalanced classification, safety-stock prediction, and entropy analysis in clinical trials. His collaborative projects reflect a commitment to both academic innovation and industrial relevance, with ongoing work in neuroimaging and supply chain resilience.

• Machine Learning
• Data Science
• Artificial Intelligence
• +۱۴ مورد دیگر

Jesse Read is a Professor (HDR) in the ORAILIX team of the Data Analytics and Machine Learning pôle at École Polytechnique, part of Institut Polytechnique de Paris. He leads research in machine learning with a focus on multi-label classification, data streams, and reinforcement learning, with applications spanning medicine, energy, and transportation systems. His research interests center around two major themes: multi-label and multi-target prediction (modeling multiple interconnected tasks), and learning from sequential data and data streams. He has particular interest in explainability, uncertainty analysis, robustness, domain shift, transfer learning, and continual learning. His methodological toolkit includes deep neural networks, probabilistic graphical models, Monte Carlo methods, and classical machine learning algorithms. Professor Read has built a strong research program with significant funding, including an ANR grant for Dynamic Graph Signal Processing in 2024. His work on Classifier Chains for Multi-label Classification received the prestigious Test of Time Award at ECML-PKDD 2019. His research bridges theoretical advances with practical applications across diverse domains. Test of Time Award at ECML-PKDD 2019 for Classifier Chains for Multi-label Classification As an educator, Read coordinates multiple courses including Deep Reinforcement Learning, Reinforcement Learning and Autonomous Agents, and Machine Learning. He co-coordinates the Trustworthy and Responsible AI (TRAI) Masters program and the Artificial Intelligence and Data Science Track. He has supervised numerous PhD students whose work spans predictive maintenance, reinforcement learning for routing problems, rail network modeling, and medical applications. His River library has become a standard tool for stream learning in Python, resulting from the merger of Creme and scikit-multiflow.

François Faureمشاهده پروفایل

استاد

• Physical Simulation for Computer Graphics
• Deformable Models
• Contact Detection and Modeling
• +۴ مورد دیگر

François Faure is a Professor of Computer Science at University Joseph Fourier, Grenoble, and a researcher at INRIA and LJK-CNRS. His work focuses on physical simulation for computer graphics, including deformable models, collision detection, and simulation software architecture. He leads the development of the SOFA simulation library, widely used in medical and entertainment applications. Education: M.Sc. Computer Science, University Paris XI (1992) Ph.D. Computer Graphics, University of Grenoble (1997) Habilitation in Computer Science, University of Grenoble (2008) Research Interests: Combines physics-based modeling with computational efficiency, emphasizing GPU acceleration and software modularity. Key topics include meshless deformation models, contact detection algorithms, and high-performance simulation frameworks. Publications: Focus on advancing simulation accuracy and efficiency, with notable work on stable constrained dynamics and frame-based deformation. Recent contributions address real-time interaction and anatomical modeling. Advising & Labs: Supervises PhD students in computer graphics and medical simulation. Active in the ANIMA and IMAGINE teams at INRIA, collaborating on virtual reality and biomechanical systems.

Zachary Tatlockمشاهده پروفایل

دانشیار

• Programming Languages
• Formal Verification
• Compiler Design
• +۴ مورد دیگر

Zachary Tatlock is an Associate Professor at the Paul G. Allen School of Computer Science & Engineering , University of Washington , with a focus on Programming Languages . His research spans formal verification , compiler design , floating-point accuracy , and machine learning frameworks , unified by themes of making tricky code easier to write and ensuring correctness through rigorous proofs and empirical systems. His recent work includes unifying Datalog and equality saturation (PLDI 2023), exploring LLM-driven document markup (Programming with AI 2024), and advancing floating-point error analysis via interactive tools like Odyssey (UIST 2023). He leads the UW PLSE research group and co-organizes events like EGRAPHS and FPTalks . Key collaborations include verifying distributed systems in CakeML (ECOOP 2022) and developing egglog , an e-graph library with relational extensions (PLDI 2023). His teaching includes Solver-Aided Programming (CSE 507) and Grad PL (CSE 505), emphasizing both Greek (formalism) and graphs (empiricism) .

Mioara Joldesمشاهده پروفایل

استاد

• Computer Arithmetic
• Validated Computing
• Computer Algebra
• +۵ مورد دیگر

Mioara Joldes is a Directeur de Recherche (DR) at CNRS working in the ROC team at LAAS-CNRS (Laboratory for Analysis and Architecture of Systems) in Toulouse, France. She has been a permanent CNRS researcher at LAAS since 2013. Her work bridges rigorous mathematical computing with practical aerospace applications, particularly in space surveillance and collision avoidance. She collaborates extensively with French space agencies like CNES and has developed algorithms that have been tested on satellites including the ESOC OPS-SAT CubeSat and embedded in the CNES ASTERIA software. Dr. Joldes received her Ph.D. from École Normale Supérieure de Lyon in 2011. In 2019, she earned her Habilitation from Toulouse III Paul Sabatier University with a dissertation focused on building efficient symbolic-numeric objects, algorithms, and software tools with space-based applications. Her academic journey reflects a strong foundation in mathematical computing translated into practical aerospace solutions. Her research spans three interconnected domains: Computer Arithmetic, where she improves the accuracy and reliability of numerical computations; Symbolic-Numeric Algorithms, developing methods that combine symbolic and numerical approaches for validated computing; and Aerospace Applications, particularly in Space Surveillance and Tracking. Her work enables mathematically rigorous error bounds for computations used in critical aerospace systems, ensuring safety in space operations through validated numerics that provide "sure, yet reasonably tight, error bounds." Her recent publications demonstrate a strong focus on collision probability computation in space, validated numerical methods for differential equations, and efficient implementations of mathematical functions. She frequently collaborates with Denis Arzelier, Florent Bréhard, and other researchers in developing algorithms that have transitioned from theoretical foundations to practical space applications, with several papers receiving significant recognition in their fields. Distinguished Paper Award at ISSAC 2019 for work on algebraic reconstruction of measures with holonomic density Best Paper Award at ASAP 2020 for efficient FPGA implementation of the Probit function CNRS Bronze medal in 2021 recognizing her research contributions Dr. Joldes leads the PRESTO International Emerging Actions Project on Probabilistic Reliable and Efficient numerics for Stochastically-constrained Trajectory Optimization. Her research has received funding from CNES and other space agencies, supporting technology transfer from theoretical mathematics to operational space systems. She has supervised multiple students working at the intersection of validated computing and aerospace applications, with her methods being implemented in operational systems used by space agencies worldwide. As a key member of the ROC (Robotique et Calcul) team at LAAS-CNRS, she develops critical software tools like CAMPARY (CudA Multiple Precision ARithmetic librarY) and contributes to Sollya, an environment for developing safe floating-point code. Her work on orbital collision probability evaluation has been featured in major publications including CNRS-Le Journal, La Tribune, Les Echos, and L'usine Nouvelle, demonstrating the real-world impact of her research in ensuring safe space operations.

Matthieu Cordمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Natural Language Processing
• +۶ مورد دیگر

Matthieu Cord is a Professor at Sorbonne University, leading research in the MLIA (Machine Learning and Artificial Intelligence) team within the Institute for Intelligent Systems and Robotics (ISIR). His work focuses on advancing transformers, multimodal learning, and neural network optimization. Research Interests: Vision-language models, multimodal transformers, efficient neural network training, and compression techniques. Article Trends: Recent publications explore in-context learning for multimodal models, diffusion-based pose estimation, and quantization methods for edge AI deployment. Laboratory Affiliation: Active in MLIA, contributing to foundational AI research with applications in computer vision and NLP.

Benoit Pirandaمشاهده پروفایل

دانشیار

• Distributed Algorithms
• Modular Robotics
• Programmable Matter
• +۴ مورد دیگر

Benoit Piranda is an Associate Professor of Computer Science at the University of Franche-Comté , affiliated with the FEMTO-ST Institute and its Complex Networks Team (DISC/OMNI) . He leads the development of VisibleSim , a parallel behavioral simulator for modular robots. University: University of Franche-Comté Institute: FEMTO-ST Team: DISC/OMNI Role: Researcher & Software Developer Research Focus: Distributed algorithms for modular robots, programmable matter, physical simulations, and parallel execution environments. His work spans self-reconfiguration, communication protocols, and efficient scene encoding for large-scale robotic systems. Article Trends: Recent publications highlight advancements in 2D/3D lattice modular robot algorithms Porous structure reconfiguration Time synchronization protocols VisibleSim simulation framework Multi-scale distributed displays Security protocols for programmable matter Conference Involvement: Active in program committees for DARS, IEEE ATC, IROS, and AINA. Former Publicity Chair positions.

Xianyi ZENGمشاهده پروفایل

استاد

• Artificial Intelligence
• Textile Digitalization
• Wearable Systems
• +۵ مورد دیگر

Xianyi ZENG is a Full Professor and Research Supervisor (Section CNU 61) at University of Lille, France, affiliated with ENSAIT (École Nationale Supérieure des Arts et Industries Textiles). His research focuses on human-centered intelligent systems in textile and fashion domains, directing the Human Centered Design Group. His primary research interests span Artificial Intelligence for textiles , Wearable healthcare systems , and Sustainable supply chain optimization . Key specialties include fabric hand evaluation through deep learning, 3D garment fitting technology, fetal monitoring garments, and carbon price forecasting. His work bridges textile engineering with computational intelligence, emphasizing practical applications in fashion digitization and sustainable manufacturing. Analysis of his 15 most recent publications reveals strong trends in AI-driven textile innovation (73% of articles), with significant focus on wearable healthcare (27%) and sustainable systems (33%). His research consistently integrates multimodal data fusion, probabilistic modeling, and human factors analysis across domains. Innovation R&D Award from French-China Committee (2021) National Knight’s title in the Order of Academic Palms (2019) Honorary Doctorate from Gheorghe Asachi Technical University (2019) TBIS2020 Plenary Medal Lecture Award (2020) Annual PEDR Award for PhD supervision since 1995 Professor ZENG has secured substantial research funding through 7 major projects as Principal Investigator, including EU Horizon 2020's FBD_BModel and French ANR's IOTFetMov. His collaborative network spans 15+ institutions across Europe and China. Current research integrates edge AI with textile systems for Industry 5.0 applications, while his teaching covers decision support systems and sensory evaluation.

• Computer Graphics
• 3D Modeling
• Virtual Reality
• +۳ مورد دیگر

Stefanie Hahmann is a Professor at Grenoble INP – Ensimag, affiliated with Université Grenoble Alpes (UGA). She is also a researcher at the Laboratoire Jean Kuntzmann (LJK) and part of the ANIMA research team, which focuses on animating objects for digital storytelling. Her work emphasizes geometric modeling and IT tools for creating realistic 3D objects and virtual worlds. Key research areas include sketch-based modeling, where 2D sketches are transformed into 3D objects, and applications in fashion design and architecture. Her research aims to develop intuitive tools for 3D creation accessible to non-experts, such as systems that virtualize clothing designs from 2D fashion sketches. She was honored with the 2022 Humboldt Prize by the Alexander von Humboldt Foundation for her contributions to scientific collaboration and innovation. Stefanie’s work bridges computer science and applied mathematics, addressing unresolved challenges like mathematically defining the 'style' of sketched objects. Her interdisciplinary approach impacts fields like animation, video games, and 3D printing.